RouteGraph-Mona: Confusion-Aware Routing Fine-Tuning for Mineral Image Classification

2026-09-02

AI Summary

为解决矿物图像分类中类内差异大和类间相似度高的问题，提出RouteGraph-Mona方法，通过自适应路由选择与基于混淆加权的正则化来优化模型。

Abstract

Mineral image classification is important for geological exploration and resource development, but it remains challenging due to substantial intra-class variations in appearance and high inter-class visual similarity. Multi-cognitive Visual Adapter (Mona) is a vision-oriented parameter-efficient adapter that adapts pre-trained visual models by tuning only a few parameters. However, Mona statically aggregates responses from multiple scales, limiting its ability to accommodate sample-specific scale preferences and model confusion among visually similar mineral categories. To address this issue, we propose \textbf{RouteGraph-Mona}, a lightweight route-space regularization method built on Mona. Specifically, we replace Mona's static multi-scale aggregation with sample-adaptive routing. The resulting branch-selection behavior defines a compact routing space that captures each image's scale preferences. We then regularize the resulting routing signatures with class-wise route anchors and confusion-weighted margins. The route anchors encourage class-consistent routing patterns, while the margins promote greater separation between visually similar categories in the routing space. Experiments on three public mineral image datasets with two visual backbones show that RouteGraph-Mona consistently outperforms Mona in mean accuracy and remains competitive with representative fine-tuning methods and mineral image classification baselines.

Problem

• Mineral Image Classification
• Intra-class Variations
• Inter-class Similarity
• Parameter-efficient Adapter

Innovation

• sample-adaptive routing
• route-space regularization
• confusion-weighted margins